danielw2904 / bayeshw Goto Github PK
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2. + 3. HW
email von florian:
"Bedingt durch die Hierarchie des Modells könnt ihr die „Likelihood“ auch anders erechnen: Ihr müsst dafür nur die Prior Dichte an der letzten Ziehung der Parameter evaluieren, also dmvnorm(parameter, prior.mean, prior.varianz, log=TRUE) bspw. Wenn ihr neue Werte für die 3 Hyperparameter proposed müsst ihr dann quasi den proposed prior mean und die prior varianz verwenden (i.e. einfach die Dummy Observations berechnen und dann solve(crossprod(Xdummy))%*%crossprod(Xdummy,Ydummy) bzw. solve(crossprod(Xdummy)) machen."
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